Overview

Dataset statistics

Number of variables12
Number of observations1366
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory128.2 KiB
Average record size in memory96.1 B

Variable types

TimeSeries12

Alerts

new_cases is highly overall correlated with new_deaths and 7 other fieldsHigh correlation
new_deaths is highly overall correlated with new_cases and 5 other fieldsHigh correlation
reproduction_rate is highly overall correlated with new_cases and 5 other fieldsHigh correlation
icu_patients is highly overall correlated with new_cases and 5 other fieldsHigh correlation
hosp_patients is highly overall correlated with new_cases and 5 other fieldsHigh correlation
new_tests is highly overall correlated with new_deaths and 4 other fieldsHigh correlation
positive_rate is highly overall correlated with new_cases and 5 other fieldsHigh correlation
people_vaccinated is highly overall correlated with population_density and 2 other fieldsHigh correlation
population_density is highly overall correlated with new_cases and 3 other fieldsHigh correlation
aged_65_older is highly overall correlated with new_cases and 3 other fieldsHigh correlation
aged_70_older is highly overall correlated with new_cases and 3 other fieldsHigh correlation
new_deaths is non stationaryNon stationary
reproduction_rate is non stationaryNon stationary
icu_patients is non stationaryNon stationary
hosp_patients is non stationaryNon stationary
new_tests is non stationaryNon stationary
positive_rate is non stationaryNon stationary
people_vaccinated is non stationaryNon stationary
population_density is non stationaryNon stationary
aged_65_older is non stationaryNon stationary
aged_70_older is non stationaryNon stationary
reproduction_rate is seasonalSeasonal
icu_patients is seasonalSeasonal
hosp_patients is seasonalSeasonal
new_tests is seasonalSeasonal
positive_rate is seasonalSeasonal
people_vaccinated is seasonalSeasonal
population_density is seasonalSeasonal
aged_65_older is seasonalSeasonal
aged_70_older is seasonalSeasonal
aged_65_older is highly skewed (γ1 = -25.71011417)Skewed
aged_70_older is highly skewed (γ1 = -25.74652501)Skewed
extreme_poverty is highly skewed (γ1 = -26.10551286)Skewed
new_deaths has 19 (1.4%) zerosZeros
reproduction_rate has 290 (21.2%) zerosZeros
icu_patients has 54 (4.0%) zerosZeros
hosp_patients has 51 (3.7%) zerosZeros
new_tests has 461 (33.7%) zerosZeros
positive_rate has 469 (34.3%) zerosZeros
people_vaccinated has 338 (24.7%) zerosZeros

Reproduction

Analysis started2023-10-02 16:43:59.678480
Analysis finished2023-10-02 16:44:14.018835
Duration14.34 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

new_cases
Numeric time series

Distinct1352
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2391306
Minimum0
Maximum39501181
Zeros13
Zeros (%)1.0%
Memory size10.8 KiB
2023-10-02T18:44:14.066122image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8682.75
Q1486812.75
median1509698
Q32457967.5
95-th percentile8003371
Maximum39501181
Range39501181
Interquartile range (IQR)1971154.8

Descriptive statistics

Standard deviation3830198.5
Coefficient of variation (CV)1.6017183
Kurtosis31.035731
Mean2391306
Median Absolute Deviation (MAD)993190.5
Skewness4.8346862
Sum3.266524 × 109
Variance1.4670421 × 1013
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.002647101845
2023-10-02T18:44:14.196062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
1.0%
4 2
 
0.1%
16 2
 
0.1%
2057076 1
 
0.1%
2862787 1
 
0.1%
2451082 1
 
0.1%
1396524 1
 
0.1%
2641781 1
 
0.1%
11210398 1
 
0.1%
2543170 1
 
0.1%
Other values (1342) 1342
98.2%
ValueCountFrequency (%)
0 13
1.0%
4 2
 
0.1%
12 1
 
0.1%
14 1
 
0.1%
16 2
 
0.1%
20 1
 
0.1%
164 1
 
0.1%
312 1
 
0.1%
318 1
 
0.1%
372 1
 
0.1%
ValueCountFrequency (%)
39501181 1
0.1%
39028694 1
0.1%
33371668 1
0.1%
33357075 1
0.1%
31447159 1
0.1%
29816600 1
0.1%
27409408 1
0.1%
27299032 1
0.1%
26943392 1
0.1%
25642776 1
0.1%
2023-10-02T18:44:14.625510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ACF and PACF

new_deaths
Numeric time series

HIGH CORRELATION  NON STATIONARY  ZEROS 

Distinct1300
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21305.586
Minimum0
Maximum127161
Zeros19
Zeros (%)1.4%
Memory size10.8 KiB
2023-10-02T18:44:14.812015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile121.5
Q14005
median19927
Q332573.25
95-th percentile54062
Maximum127161
Range127161
Interquartile range (IQR)28568.25

Descriptive statistics

Standard deviation19948.296
Coefficient of variation (CV)0.93629419
Kurtosis3.7336025
Mean21305.586
Median Absolute Deviation (MAD)14658
Skewness1.4799899
Sum29103430
Variance3.9793452 × 108
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.2195432099
2023-10-02T18:44:14.919036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
1.4%
4 7
 
0.5%
8 4
 
0.3%
233 3
 
0.2%
71 3
 
0.2%
33354 2
 
0.1%
273 2
 
0.1%
376 2
 
0.1%
1245 2
 
0.1%
107 2
 
0.1%
Other values (1290) 1320
96.6%
ValueCountFrequency (%)
0 19
1.4%
4 7
 
0.5%
8 4
 
0.3%
10 1
 
0.1%
12 1
 
0.1%
15 1
 
0.1%
20 1
 
0.1%
27 1
 
0.1%
32 1
 
0.1%
44 1
 
0.1%
ValueCountFrequency (%)
127161 1
0.1%
121747 1
0.1%
119109 1
0.1%
118854 1
0.1%
118526 1
0.1%
118007 1
0.1%
111691 1
0.1%
110867 1
0.1%
107400 1
0.1%
107233 1
0.1%
2023-10-02T18:44:15.450416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ACF and PACF

reproduction_rate
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct990
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.32346
Minimum0
Maximum247.3
Zeros290
Zeros (%)21.2%
Memory size10.8 KiB
2023-10-02T18:44:15.674470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q169.1175
median153.665
Q3176.1075
95-th percentile197.8575
Maximum247.3
Range247.3
Interquartile range (IQR)106.99

Descriptive statistics

Standard deviation73.83393
Coefficient of variation (CV)0.59870144
Kurtosis-0.8678303
Mean123.32346
Median Absolute Deviation (MAD)26.05
Skewness-0.8463727
Sum168459.84
Variance5451.4493
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.538229496
2023-10-02T18:44:15.803336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 290
 
21.2%
172.73 3
 
0.2%
170.38 3
 
0.2%
178.27 2
 
0.1%
171.28 2
 
0.1%
170.67 2
 
0.1%
171.05 2
 
0.1%
175.62 2
 
0.1%
176.43 2
 
0.1%
173.82 2
 
0.1%
Other values (980) 1056
77.3%
ValueCountFrequency (%)
0 290
21.2%
0.96 1
 
0.1%
1.17 1
 
0.1%
1.35 1
 
0.1%
1.63 1
 
0.1%
1.96 1
 
0.1%
2.42 1
 
0.1%
2.82 1
 
0.1%
2.85 2
 
0.1%
3.01 2
 
0.1%
ValueCountFrequency (%)
247.3 1
0.1%
246.94 1
0.1%
246.31 1
0.1%
244.77 1
0.1%
244.16 1
0.1%
242.54 1
0.1%
241.92 1
0.1%
241.89 1
0.1%
241.79 1
0.1%
240.68 1
0.1%
2023-10-02T18:44:16.270365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ACF and PACF

icu_patients
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct1279
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18599.351
Minimum0
Maximum64534
Zeros54
Zeros (%)4.0%
Memory size10.8 KiB
2023-10-02T18:44:16.481966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile171
Q15732.25
median10873.5
Q331703.75
95-th percentile49423.75
Maximum64534
Range64534
Interquartile range (IQR)25971.5

Descriptive statistics

Standard deviation16553.926
Coefficient of variation (CV)0.89002712
Kurtosis-0.4670441
Mean18599.351
Median Absolute Deviation (MAD)9463.5
Skewness0.8008415
Sum25406713
Variance2.7403248 × 108
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.316300242
2023-10-02T18:44:16.602517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54
 
4.0%
6071 3
 
0.2%
16874 2
 
0.1%
6626 2
 
0.1%
1200 2
 
0.1%
5333 2
 
0.1%
4984 2
 
0.1%
5020 2
 
0.1%
1769 2
 
0.1%
11765 2
 
0.1%
Other values (1269) 1293
94.7%
ValueCountFrequency (%)
0 54
4.0%
8 1
 
0.1%
10 1
 
0.1%
18 1
 
0.1%
19 1
 
0.1%
22 2
 
0.1%
26 1
 
0.1%
27 1
 
0.1%
35 1
 
0.1%
36 1
 
0.1%
ValueCountFrequency (%)
64534 1
0.1%
64210 1
0.1%
64002 1
0.1%
63084 1
0.1%
63000 1
0.1%
62996 1
0.1%
62978 1
0.1%
62966 1
0.1%
62811 1
0.1%
62777 1
0.1%
2023-10-02T18:44:17.067080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ACF and PACF

hosp_patients
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct1307
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111657.25
Minimum0
Maximum363932
Zeros51
Zeros (%)3.7%
Memory size10.8 KiB
2023-10-02T18:44:17.279037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1209.75
Q156808
median93951
Q3151361.75
95-th percentile292081.25
Maximum363932
Range363932
Interquartile range (IQR)94553.75

Descriptive statistics

Standard deviation82450.504
Coefficient of variation (CV)0.73842498
Kurtosis0.48990952
Mean111657.25
Median Absolute Deviation (MAD)47656
Skewness0.96552984
Sum1.5252381 × 108
Variance6.7980856 × 109
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.1170527935
2023-10-02T18:44:17.407126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 51
 
3.7%
1 3
 
0.2%
78856 2
 
0.1%
26777 2
 
0.1%
162109 2
 
0.1%
62063 2
 
0.1%
69403 2
 
0.1%
12881 2
 
0.1%
197561 2
 
0.1%
122245 1
 
0.1%
Other values (1297) 1297
94.9%
ValueCountFrequency (%)
0 51
3.7%
1 3
 
0.2%
116 1
 
0.1%
127 1
 
0.1%
146 1
 
0.1%
151 1
 
0.1%
165 1
 
0.1%
305 1
 
0.1%
360 1
 
0.1%
367 1
 
0.1%
ValueCountFrequency (%)
363932 1
0.1%
358785 1
0.1%
356423 1
0.1%
352117 1
0.1%
348415 1
0.1%
348294 1
0.1%
348203 1
0.1%
347719 1
0.1%
347324 1
0.1%
346888 1
0.1%
2023-10-02T18:44:17.910863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ACF and PACF

new_tests
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct905
Distinct (%)66.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3714144.9
Minimum0
Maximum40004361
Zeros461
Zeros (%)33.7%
Memory size10.8 KiB
2023-10-02T18:44:18.104700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2568733
Q37025344.8
95-th percentile10239692
Maximum40004361
Range40004361
Interquartile range (IQR)7025344.8

Descriptive statistics

Standard deviation4043172
Coefficient of variation (CV)1.0885876
Kurtosis3.9891303
Mean3714144.9
Median Absolute Deviation (MAD)2568733
Skewness1.1570835
Sum5.0735219 × 109
Variance1.634724 × 1013
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.6653825112
2023-10-02T18:44:18.253439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 461
33.7%
175 2
 
0.1%
9622180 1
 
0.1%
9843011 1
 
0.1%
8191464 1
 
0.1%
6459196 1
 
0.1%
7907851 1
 
0.1%
9329052 1
 
0.1%
9517655 1
 
0.1%
10119314 1
 
0.1%
Other values (895) 895
65.5%
ValueCountFrequency (%)
0 461
33.7%
29 1
 
0.1%
49 1
 
0.1%
94 1
 
0.1%
96 1
 
0.1%
97 1
 
0.1%
123 1
 
0.1%
167 1
 
0.1%
172 1
 
0.1%
175 2
 
0.1%
ValueCountFrequency (%)
40004361 1
0.1%
16610100 1
0.1%
16188778 1
0.1%
16188243 1
0.1%
16024393 1
0.1%
15920713 1
0.1%
15894016 1
0.1%
15614258 1
0.1%
15284537 1
0.1%
15227913 1
0.1%
2023-10-02T18:44:18.762684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ACF and PACF

positive_rate
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct893
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8934368
Minimum0
Maximum31.53
Zeros469
Zeros (%)34.3%
Memory size10.8 KiB
2023-10-02T18:44:18.979166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8.1848
Q311.134825
95-th percentile17.7896
Maximum31.53
Range31.53
Interquartile range (IQR)11.134825

Descriptive statistics

Standard deviation6.692481
Coefficient of variation (CV)0.97084824
Kurtosis1.0571928
Mean6.8934368
Median Absolute Deviation (MAD)4.695
Skewness0.8813799
Sum9416.4347
Variance44.789302
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.1731009777
2023-10-02T18:44:19.112258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 469
34.3%
0.026 2
 
0.1%
12.8412 2
 
0.1%
9.4435 2
 
0.1%
0.2004 2
 
0.1%
10.4614 2
 
0.1%
11.428 1
 
0.1%
12.3304 1
 
0.1%
11.9959 1
 
0.1%
13.2038 1
 
0.1%
Other values (883) 883
64.6%
ValueCountFrequency (%)
0 469
34.3%
0.026 2
 
0.1%
0.0294 1
 
0.1%
0.0302 1
 
0.1%
0.031 1
 
0.1%
0.038 1
 
0.1%
0.042 1
 
0.1%
0.049 1
 
0.1%
0.05 1
 
0.1%
0.076 1
 
0.1%
ValueCountFrequency (%)
31.53 1
0.1%
30.6225 1
0.1%
30.432 1
0.1%
30.3182 1
0.1%
30.2324 1
0.1%
29.9115 1
0.1%
29.8175 1
0.1%
29.782 1
0.1%
29.6344 1
0.1%
29.6234 1
0.1%
2023-10-02T18:44:19.567905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ACF and PACF

people_vaccinated
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct1028
Distinct (%)75.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0658198 × 1010
Minimum0
Maximum2.0521728 × 1010
Zeros338
Zeros (%)24.7%
Memory size10.8 KiB
2023-10-02T18:44:19.779500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114.5
median1.4981407 × 1010
Q31.8702228 × 1010
95-th percentile1.9178431 × 1010
Maximum2.0521728 × 1010
Range2.0521728 × 1010
Interquartile range (IQR)1.8702228 × 1010

Descriptive statistics

Standard deviation8.5300688 × 109
Coefficient of variation (CV)0.80032938
Kurtosis-1.7884883
Mean1.0658198 × 1010
Median Absolute Deviation (MAD)4.1620046 × 109
Skewness-0.27081473
Sum1.4559098 × 1013
Variance7.2762074 × 1019
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.6947824103
2023-10-02T18:44:19.897850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 338
 
24.7%
4 2
 
0.1%
1.892215803 × 10101
 
0.1%
1.904951449 × 10101
 
0.1%
1.898268738 × 10101
 
0.1%
1.886053076 × 10101
 
0.1%
1.897340489 × 10101
 
0.1%
1.907878553 × 10101
 
0.1%
1.896818696 × 10101
 
0.1%
1.899064081 × 10101
 
0.1%
Other values (1018) 1018
74.5%
ValueCountFrequency (%)
0 338
24.7%
4 2
 
0.1%
5 1
 
0.1%
10 1
 
0.1%
28 1
 
0.1%
39 1
 
0.1%
40 1
 
0.1%
56 1
 
0.1%
60 1
 
0.1%
147316 1
 
0.1%
ValueCountFrequency (%)
2.052172793 × 10101
0.1%
2.047270104 × 10101
0.1%
2.041170943 × 10101
0.1%
2.040778457 × 10101
0.1%
2.034131371 × 10101
0.1%
2.030446307 × 10101
0.1%
2.025998364 × 10101
0.1%
2.023128478 × 10101
0.1%
2.021771874 × 10101
0.1%
2.018900507 × 10101
0.1%
2023-10-02T18:44:20.367790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ACF and PACF

population_density
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86687.695
Minimum82.621
Maximum95669.736
Zeros0
Zeros (%)0.0%
Memory size10.8 KiB
2023-10-02T18:44:20.577953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum82.621
5-th percentile75122.97
Q175122.97
median95669.736
Q395669.736
95-th percentile95669.736
Maximum95669.736
Range95587.115
Interquartile range (IQR)20546.766

Descriptive statistics

Standard deviation11058.282
Coefficient of variation (CV)0.12756461
Kurtosis3.5610736
Mean86687.695
Median Absolute Deviation (MAD)0
Skewness-1.0157824
Sum1.1841539 × 108
Variance1.2228561 × 108
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.487573366 × 10-12
2023-10-02T18:44:20.686097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
95669.736 795
58.2%
75122.97 514
37.6%
68083.256 55
 
4.0%
82.621 2
 
0.1%
ValueCountFrequency (%)
82.621 2
 
0.1%
68083.256 55
 
4.0%
75122.97 514
37.6%
95669.736 795
58.2%
ValueCountFrequency (%)
95669.736 795
58.2%
75122.97 514
37.6%
68083.256 55
 
4.0%
82.621 2
 
0.1%
2023-10-02T18:44:21.113301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ACF and PACF

aged_65_older
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  SKEWED 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1672.4515
Minimum18.055
Maximum1679.622
Zeros0
Zeros (%)0.0%
Memory size10.8 KiB
2023-10-02T18:44:21.339269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18.055
5-th percentile1669.824
Q11669.824
median1679.622
Q31679.622
95-th percentile1679.622
Maximum1679.622
Range1661.567
Interquartile range (IQR)9.798

Descriptive statistics

Standard deviation63.696797
Coefficient of variation (CV)0.038085885
Kurtosis666.71554
Mean1672.4515
Median Absolute Deviation (MAD)0
Skewness-25.710114
Sum2284568.8
Variance4057.282
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-10-02T18:44:21.430044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
1679.622 795
58.2%
1669.824 514
37.6%
1653.521 55
 
4.0%
18.055 2
 
0.1%
ValueCountFrequency (%)
18.055 2
 
0.1%
1653.521 55
 
4.0%
1669.824 514
37.6%
1679.622 795
58.2%
ValueCountFrequency (%)
1679.622 795
58.2%
1669.824 514
37.6%
1653.521 55
 
4.0%
18.055 2
 
0.1%
2023-10-02T18:44:21.912129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ACF and PACF

aged_70_older
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  SKEWED 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1084.6303
Minimum11.762
Maximum1090.172
Zeros0
Zeros (%)0.0%
Memory size10.8 KiB
2023-10-02T18:44:22.137192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum11.762
5-th percentile1083.801
Q11083.801
median1088.792
Q31088.792
95-th percentile1088.792
Maximum1090.172
Range1078.41
Interquartile range (IQR)4.991

Descriptive statistics

Standard deviation41.288068
Coefficient of variation (CV)0.03806649
Kurtosis667.94967
Mean1084.6303
Median Absolute Deviation (MAD)0
Skewness-25.746525
Sum1481605
Variance1704.7046
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9212631403
2023-10-02T18:44:22.239332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1088.792 794
58.1%
1083.801 514
37.6%
1073.643 39
 
2.9%
1065.29 16
 
1.2%
11.762 2
 
0.1%
1090.172 1
 
0.1%
ValueCountFrequency (%)
11.762 2
 
0.1%
1065.29 16
 
1.2%
1073.643 39
 
2.9%
1083.801 514
37.6%
1088.792 794
58.1%
1090.172 1
 
0.1%
ValueCountFrequency (%)
1090.172 1
 
0.1%
1088.792 794
58.1%
1083.801 514
37.6%
1073.643 39
 
2.9%
1065.29 16
 
1.2%
11.762 2
 
0.1%
2023-10-02T18:44:22.729788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ACF and PACF

extreme_poverty
Numeric time series

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1742.3498
Minimum3.1
Maximum1744.9
Zeros0
Zeros (%)0.0%
Memory size10.8 KiB
2023-10-02T18:44:22.958419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.1
5-th percentile1744.9
Q11744.9
median1744.9
Q31744.9
95-th percentile1744.9
Maximum1744.9
Range1741.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation66.623707
Coefficient of variation (CV)0.038237849
Kurtosis680.49413
Mean1742.3498
Median Absolute Deviation (MAD)0
Skewness-26.105513
Sum2380049.8
Variance4438.7183
MonotonicityIncreasing
Augmented Dickey-Fuller test p-value0
2023-10-02T18:44:23.058113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
1744.9 1364
99.9%
3.1 2
 
0.1%
ValueCountFrequency (%)
3.1 2
 
0.1%
1744.9 1364
99.9%
ValueCountFrequency (%)
1744.9 1364
99.9%
3.1 2
 
0.1%
2023-10-02T18:44:23.510989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ACF and PACF

Interactions

2023-10-02T18:44:12.066461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:00.160116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:01.063351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:02.073733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:02.917365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:03.864505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:05.139844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:06.368883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:07.653259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:08.879216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:09.882883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:10.903652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:12.186607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:00.239902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:01.142045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:02.147460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:02.990161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:03.968934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:05.244124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:06.477803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:07.760230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:08.967261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:09.974251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:10.993247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:12.310786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:00.314753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:01.213986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:02.216919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:03.068005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:04.055055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:05.337909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:06.591926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:07.859154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:09.046026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:10.059265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:11.070906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:12.431082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:00.388845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:01.303894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:02.280273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:03.136843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:04.277387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:05.429336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:06.699399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:07.950354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:09.124798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:10.138433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:11.149061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:12.547981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:00.460275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:01.501742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:02.354465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:03.207363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:04.368302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:05.524691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:06.802516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:08.046088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:09.210995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:10.222846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:11.228866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:12.670979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:00.536299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:01.572677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:02.432053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:03.295188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:04.460532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:05.622280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:06.909338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:08.137511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:09.300191image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:10.310589image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:11.313320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:12.805429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:00.615668image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:01.654934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:02.505963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:03.372146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:04.563892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:05.722263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:07.016639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:08.228804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:09.392015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:10.403321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:11.399871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:12.920379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:00.689016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:01.723094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:02.579097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:03.444111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:04.659678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:05.822664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:07.119098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:08.311212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:09.474988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:10.488276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:11.479360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:13.045178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:00.764512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:01.792998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:02.648696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:03.522834image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:04.747017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:05.930584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:07.222987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:08.389327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:09.556474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:10.567891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:11.567013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:13.160550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:00.840166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:01.862245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:02.713206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:03.604036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:04.840259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:06.036654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:07.335557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:08.638535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:09.636237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:10.648569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:11.707341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:13.285794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:00.917903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:01.934638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:02.783732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:03.692013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:04.943239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:06.150337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:07.447433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:08.719242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:09.720205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:10.735333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:11.834186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:13.377095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:00.988616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:02.002144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:02.847719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:03.775497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:05.033928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:06.254148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:07.548315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:08.797327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:09.799530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:10.817889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-10-02T18:44:11.960883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-10-02T18:44:23.720357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
new_casesnew_deathsreproduction_rateicu_patientshosp_patientsnew_testspositive_ratepeople_vaccinatedpopulation_densityaged_65_olderaged_70_olderextreme_poverty
new_cases1.0000.6700.5430.6840.7830.4870.5360.1800.6220.6220.6220.066
new_deaths0.6701.0000.7610.9010.7370.7710.693-0.3990.2800.2800.2790.065
reproduction_rate0.5430.7611.0000.8040.6260.7960.735-0.4770.2190.2190.2190.052
icu_patients0.6840.9010.8041.0000.8930.7950.728-0.3170.3320.3320.3320.064
hosp_patients0.7830.7370.6260.8931.0000.6220.659-0.0240.4850.4850.4850.064
new_tests0.4870.7710.7960.7950.6221.0000.878-0.4550.2590.2590.2590.022
positive_rate0.5360.6930.7350.7280.6590.8781.000-0.3890.1910.1910.1920.044
people_vaccinated0.180-0.399-0.477-0.317-0.024-0.455-0.3891.0000.5470.5470.5470.050
population_density0.6220.2800.2190.3320.4850.2590.1910.5471.0001.0000.9990.076
aged_65_older0.6220.2800.2190.3320.4850.2590.1910.5471.0001.0000.9990.076
aged_70_older0.6220.2790.2190.3320.4850.2590.1920.5470.9990.9991.0000.076
extreme_poverty0.0660.0650.0520.0640.0640.0220.0440.0500.0760.0760.0761.000

Missing values

2023-10-02T18:44:13.539013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-02T18:44:13.949779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

new_casesnew_deathsreproduction_rateicu_patientshosp_patientsnew_testspositive_ratepeople_vaccinatedpopulation_densityaged_65_olderaged_70_olderextreme_poverty
00.00.00.00.00.029.00.0000.082.62118.05511.7623.1
10.00.00.00.00.0167.00.0000.082.62118.05511.7623.1
20.00.00.00.00.094.00.0000.068083.2561653.5211065.2901744.9
314.00.00.00.00.0123.00.0000.068083.2561653.5211065.2901744.9
40.015.00.00.00.097.00.0000.068083.2561653.5211065.2901744.9
512.00.00.00.01.0225.00.0000.068083.2561653.5211065.2901744.9
60.00.00.00.00.0215.00.1680.068083.2561653.5211065.2901744.9
74.00.00.00.00.0175.00.1640.068083.2561653.5211065.2901744.9
80.00.00.00.00.0205.00.1400.068083.2561653.5211065.2901744.9
90.00.00.00.00.0244.00.1040.068083.2561653.5211065.2901744.9
new_casesnew_deathsreproduction_rateicu_patientshosp_patientsnew_testspositive_ratepeople_vaccinatedpopulation_densityaged_65_olderaged_70_olderextreme_poverty
135673429.0309.00.0211.06191.00.00.01.797442e+1068083.2561653.5211073.6431744.9
135778046.0321.00.0208.05891.00.00.01.816044e+1068083.2561653.5211073.6431744.9
1358552.04.00.0454.021701.00.00.01.773175e+1068083.2561653.5211073.6431744.9
1359536.00.00.027.01188.00.00.01.758025e+1068083.2561653.5211073.6431744.9
13603840.04.00.022.0556.00.00.01.758034e+1068083.2561653.5211073.6431744.9
13612388.08.00.010.0146.00.00.01.773281e+1068083.2561653.5211073.6431744.9
13624240.088.00.08.0116.00.00.01.772418e+1068083.2561653.5211073.6431744.9
13630.00.00.018.0360.00.00.01.754046e+1068083.2561653.5211065.2901744.9
13640.00.00.022.0367.00.00.01.778454e+1068083.2561653.5211065.2901744.9
13650.00.00.019.0368.00.00.01.757173e+1068083.2561653.5211065.2901744.9